Overview

Dataset statistics

Number of variables31
Number of observations199551
Missing cells348463
Missing cells (%)5.6%
Duplicate rows2
Duplicate rows (%)< 0.1%
Total size in memory38.1 MiB
Average record size in memory200.0 B

Variable types

Categorical8
DateTime5
Numeric10
Boolean8

Alerts

Dataset has 2 (< 0.1%) duplicate rowsDuplicates
Valor del Contrato is highly overall correlated with Saldo CDPHigh correlation
Valor Facturado is highly overall correlated with Valor Pendiente de Pago and 2 other fieldsHigh correlation
Valor Pendiente de Pago is highly overall correlated with Valor Facturado and 2 other fieldsHigh correlation
Valor Pagado is highly overall correlated with Valor Facturado and 2 other fieldsHigh correlation
Valor Pendiente de Ejecucion is highly overall correlated with Valor Facturado and 2 other fieldsHigh correlation
Saldo CDP is highly overall correlated with Valor del ContratoHigh correlation
Modalidad de Contratacion is highly overall correlated with EsPrestacionServicios and 1 other fieldsHigh correlation
EsPrestacionServicios is highly overall correlated with Modalidad de ContratacionHigh correlation
EsPyme is highly overall correlated with Modalidad de ContratacionHigh correlation
Rama is highly imbalanced (64.5%)Imbalance
Modalidad de Contratacion is highly imbalanced (67.7%)Imbalance
EsGrupo is highly imbalanced (94.5%)Imbalance
EsObligacionAmbiental is highly imbalanced (76.4%)Imbalance
Es PostConflicto is highly imbalanced (95.6%)Imbalance
Fecha de Inicio de Ejecucion has 174156 (87.3%) missing valuesMissing
Fecha de Fin de Ejecucion has 173961 (87.2%) missing valuesMissing
Valor del Contrato is highly skewed (γ1 = 397.1971247)Skewed
Valor Facturado is highly skewed (γ1 = 439.6718332)Skewed
Valor Pendiente de Pago is highly skewed (γ1 = 228.5471793)Skewed
Valor Pagado is highly skewed (γ1 = 439.3312176)Skewed
Valor Pendiente de Ejecucion is highly skewed (γ1 = 122.4992687)Skewed
Saldo CDP is highly skewed (γ1 = 220.0639176)Skewed
Dias Adicionados is highly skewed (γ1 = 35.4033165)Skewed
Presupuesto General de la Nacion – PGN is highly skewed (γ1 = 437.9613202)Skewed
Recursos Propios (Alcaldías, Gobernaciones y Resguardos Indígenas) is highly skewed (γ1 = 169.3895246)Skewed
Recursos Propios is highly skewed (γ1 = 149.500941)Skewed
Valor Facturado has 82836 (41.5%) zerosZeros
Valor Pendiente de Pago has 73431 (36.8%) zerosZeros
Valor Pagado has 93285 (46.7%) zerosZeros
Valor Pendiente de Ejecucion has 73005 (36.6%) zerosZeros
Saldo CDP has 10520 (5.3%) zerosZeros
Dias Adicionados has 198518 (99.5%) zerosZeros
Presupuesto General de la Nacion – PGN has 161214 (80.8%) zerosZeros
Recursos Propios (Alcaldías, Gobernaciones y Resguardos Indígenas) has 110760 (55.5%) zerosZeros
Recursos Propios has 165174 (82.8%) zerosZeros

Reproduction

Analysis started2023-07-20 00:24:44.276290
Analysis finished2023-07-20 00:25:32.886275
Duration48.61 seconds
Software versionydata-profiling vv4.2.0
Download configurationconfig.json

Variables

Departamento
Categorical

Distinct34
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.0 MiB
Distrito Capital de Bogotá
58836 
Valle del Cauca
23048 
Antioquia
19788 
Santander
14842 
Meta
9185 
Other values (29)
73852 

Length

Max length40
Median length26
Mean length14.434571
Min length4

Characters and Unicode

Total characters2880433
Distinct characters45
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAntioquia
2nd rowDistrito Capital de Bogotá
3rd rowSantander
4th rowBoyacá
5th rowAntioquia

Common Values

ValueCountFrequency (%)
Distrito Capital de Bogotá 58836
29.5%
Valle del Cauca 23048
 
11.5%
Antioquia 19788
 
9.9%
Santander 14842
 
7.4%
Meta 9185
 
4.6%
Boyacá 8682
 
4.4%
Risaralda 6715
 
3.4%
Cundinamarca 6606
 
3.3%
Huila 5990
 
3.0%
Casanare 5618
 
2.8%
Other values (24) 40241
20.2%

Length

2023-07-19T19:25:33.041277image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
de 63810
14.4%
distrito 58836
13.3%
capital 58836
13.3%
bogotá 58836
13.3%
cauca 25583
 
5.8%
valle 23048
 
5.2%
del 23048
 
5.2%
santander 19816
 
4.5%
antioquia 19788
 
4.5%
meta 9185
 
2.1%
Other values (35) 81573
18.4%

Most occurring characters

ValueCountFrequency (%)
a 377058
13.1%
t 301182
 
10.5%
i 255774
 
8.9%
242808
 
8.4%
o 231785
 
8.0%
l 159014
 
5.5%
e 158162
 
5.5%
d 133791
 
4.6%
r 117918
 
4.1%
C 106494
 
3.7%
Other values (35) 796447
27.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2282124
79.2%
Uppercase Letter 353678
 
12.3%
Space Separator 242808
 
8.4%
Other Punctuation 1823
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 377058
16.5%
t 301182
13.2%
i 255774
11.2%
o 231785
10.2%
l 159014
7.0%
e 158162
6.9%
d 133791
 
5.9%
r 117918
 
5.2%
n 96990
 
4.2%
s 78955
 
3.5%
Other values (18) 371495
16.3%
Uppercase Letter
ValueCountFrequency (%)
C 106494
30.1%
B 71563
20.2%
D 59593
16.8%
A 26843
 
7.6%
S 24041
 
6.8%
V 23304
 
6.6%
M 10960
 
3.1%
N 7541
 
2.1%
R 6715
 
1.9%
H 5990
 
1.7%
Other values (5) 10634
 
3.0%
Space Separator
ValueCountFrequency (%)
242808
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1823
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2635802
91.5%
Common 244631
 
8.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 377058
14.3%
t 301182
11.4%
i 255774
 
9.7%
o 231785
 
8.8%
l 159014
 
6.0%
e 158162
 
6.0%
d 133791
 
5.1%
r 117918
 
4.5%
C 106494
 
4.0%
n 96990
 
3.7%
Other values (33) 697634
26.5%
Common
ValueCountFrequency (%)
242808
99.3%
, 1823
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2795801
97.1%
None 84632
 
2.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 377058
13.5%
t 301182
10.8%
i 255774
 
9.1%
242808
 
8.7%
o 231785
 
8.3%
l 159014
 
5.7%
e 158162
 
5.7%
d 133791
 
4.8%
r 117918
 
4.2%
C 106494
 
3.8%
Other values (30) 711815
25.5%
None
ValueCountFrequency (%)
á 72117
85.2%
í 7426
 
8.8%
é 1962
 
2.3%
ñ 1810
 
2.1%
ó 1317
 
1.6%

Orden
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.0 MiB
Territorial
126118 
Nacional
70175 
Corporación Autónoma
 
3258

Length

Max length20
Median length11
Mean length10.091946
Min length8

Characters and Unicode

Total characters2013858
Distinct characters18
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNacional
2nd rowNacional
3rd rowCorporación Autónoma
4th rowTerritorial
5th rowTerritorial

Common Values

ValueCountFrequency (%)
Territorial 126118
63.2%
Nacional 70175
35.2%
Corporación Autónoma 3258
 
1.6%

Length

2023-07-19T19:25:33.260844image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-19T19:25:33.493922image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
territorial 126118
62.2%
nacional 70175
34.6%
corporación 3258
 
1.6%
autónoma 3258
 
1.6%

Most occurring characters

ValueCountFrequency (%)
r 384870
19.1%
i 325669
16.2%
a 272984
13.6%
o 206067
10.2%
l 196293
9.7%
t 129376
 
6.4%
T 126118
 
6.3%
e 126118
 
6.3%
n 76691
 
3.8%
c 73433
 
3.6%
Other values (8) 96239
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1807791
89.8%
Uppercase Letter 202809
 
10.1%
Space Separator 3258
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 384870
21.3%
i 325669
18.0%
a 272984
15.1%
o 206067
11.4%
l 196293
10.9%
t 129376
 
7.2%
e 126118
 
7.0%
n 76691
 
4.2%
c 73433
 
4.1%
ó 6516
 
0.4%
Other values (3) 9774
 
0.5%
Uppercase Letter
ValueCountFrequency (%)
T 126118
62.2%
N 70175
34.6%
C 3258
 
1.6%
A 3258
 
1.6%
Space Separator
ValueCountFrequency (%)
3258
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2010600
99.8%
Common 3258
 
0.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 384870
19.1%
i 325669
16.2%
a 272984
13.6%
o 206067
10.2%
l 196293
9.8%
t 129376
 
6.4%
T 126118
 
6.3%
e 126118
 
6.3%
n 76691
 
3.8%
c 73433
 
3.7%
Other values (7) 92981
 
4.6%
Common
ValueCountFrequency (%)
3258
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2007342
99.7%
None 6516
 
0.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 384870
19.2%
i 325669
16.2%
a 272984
13.6%
o 206067
10.3%
l 196293
9.8%
t 129376
 
6.4%
T 126118
 
6.3%
e 126118
 
6.3%
n 76691
 
3.8%
c 73433
 
3.7%
Other values (7) 89723
 
4.5%
None
ValueCountFrequency (%)
ó 6516
100.0%

Rama
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.0 MiB
Ejecutivo
169056 
Corporación Autónoma
27123 
Judicial
 
1784
Legislativo
 
1588

Length

Max length20
Median length9
Mean length10.502097
Min length8

Characters and Unicode

Total characters2095704
Distinct characters24
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCorporación Autónoma
2nd rowEjecutivo
3rd rowCorporación Autónoma
4th rowEjecutivo
5th rowEjecutivo

Common Values

ValueCountFrequency (%)
Ejecutivo 169056
84.7%
Corporación Autónoma 27123
 
13.6%
Judicial 1784
 
0.9%
Legislativo 1588
 
0.8%

Length

2023-07-19T19:25:33.683925image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-19T19:25:33.911046image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
ejecutivo 169056
74.6%
corporación 27123
 
12.0%
autónoma 27123
 
12.0%
judicial 1784
 
0.8%
legislativo 1588
 
0.7%

Most occurring characters

ValueCountFrequency (%)
o 252013
12.0%
i 202923
9.7%
c 197963
9.4%
u 197963
9.4%
t 197767
9.4%
e 170644
8.1%
v 170644
8.1%
E 169056
8.1%
j 169056
8.1%
a 57618
 
2.7%
Other values (14) 310057
14.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1841907
87.9%
Uppercase Letter 226674
 
10.8%
Space Separator 27123
 
1.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 252013
13.7%
i 202923
11.0%
c 197963
10.7%
u 197963
10.7%
t 197767
10.7%
e 170644
9.3%
v 170644
9.3%
j 169056
9.2%
a 57618
 
3.1%
r 54246
 
2.9%
Other values (8) 171070
9.3%
Uppercase Letter
ValueCountFrequency (%)
E 169056
74.6%
C 27123
 
12.0%
A 27123
 
12.0%
J 1784
 
0.8%
L 1588
 
0.7%
Space Separator
ValueCountFrequency (%)
27123
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2068581
98.7%
Common 27123
 
1.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 252013
12.2%
i 202923
9.8%
c 197963
9.6%
u 197963
9.6%
t 197767
9.6%
e 170644
8.2%
v 170644
8.2%
E 169056
8.2%
j 169056
8.2%
a 57618
 
2.8%
Other values (13) 282934
13.7%
Common
ValueCountFrequency (%)
27123
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2041458
97.4%
None 54246
 
2.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 252013
12.3%
i 202923
9.9%
c 197963
9.7%
u 197963
9.7%
t 197767
9.7%
e 170644
8.4%
v 170644
8.4%
E 169056
8.3%
j 169056
8.3%
a 57618
 
2.8%
Other values (13) 255811
12.5%
None
ValueCountFrequency (%)
ó 54246
100.0%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.0 MiB
Descentralizada
104790 
Centralizada
86527 
No Definido
 
8234

Length

Max length15
Median length15
Mean length13.534124
Min length11

Characters and Unicode

Total characters2700748
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCentralizada
2nd rowCentralizada
3rd rowCentralizada
4th rowDescentralizada
5th rowDescentralizada

Common Values

ValueCountFrequency (%)
Descentralizada 104790
52.5%
Centralizada 86527
43.4%
No Definido 8234
 
4.1%

Length

2023-07-19T19:25:34.114861image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-19T19:25:34.337864image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
descentralizada 104790
50.4%
centralizada 86527
41.6%
no 8234
 
4.0%
definido 8234
 
4.0%

Most occurring characters

ValueCountFrequency (%)
a 573951
21.3%
e 304341
11.3%
i 207785
 
7.7%
d 199551
 
7.4%
n 199551
 
7.4%
l 191317
 
7.1%
z 191317
 
7.1%
t 191317
 
7.1%
r 191317
 
7.1%
D 113024
 
4.2%
Other values (7) 337277
12.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2484729
92.0%
Uppercase Letter 207785
 
7.7%
Space Separator 8234
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 573951
23.1%
e 304341
12.2%
i 207785
 
8.4%
d 199551
 
8.0%
n 199551
 
8.0%
l 191317
 
7.7%
z 191317
 
7.7%
t 191317
 
7.7%
r 191317
 
7.7%
c 104790
 
4.2%
Other values (3) 129492
 
5.2%
Uppercase Letter
ValueCountFrequency (%)
D 113024
54.4%
C 86527
41.6%
N 8234
 
4.0%
Space Separator
ValueCountFrequency (%)
8234
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2692514
99.7%
Common 8234
 
0.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 573951
21.3%
e 304341
11.3%
i 207785
 
7.7%
d 199551
 
7.4%
n 199551
 
7.4%
l 191317
 
7.1%
z 191317
 
7.1%
t 191317
 
7.1%
r 191317
 
7.1%
D 113024
 
4.2%
Other values (6) 329043
12.2%
Common
ValueCountFrequency (%)
8234
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2700748
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 573951
21.3%
e 304341
11.3%
i 207785
 
7.7%
d 199551
 
7.4%
n 199551
 
7.4%
l 191317
 
7.1%
z 191317
 
7.1%
t 191317
 
7.1%
r 191317
 
7.1%
D 113024
 
4.2%
Other values (7) 337277
12.5%

Estado Contrato
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.0 MiB
terminado
125044 
Cerrado
65834 
cedido
 
7404
Suspendido
 
1269

Length

Max length10
Median length9
Mean length8.2352281
Min length6

Characters and Unicode

Total characters1643348
Distinct characters15
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCerrado
2nd rowcedido
3rd rowCerrado
4th rowterminado
5th rowterminado

Common Values

ValueCountFrequency (%)
terminado 125044
62.7%
Cerrado 65834
33.0%
cedido 7404
 
3.7%
Suspendido 1269
 
0.6%

Length

2023-07-19T19:25:34.547944image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-19T19:25:34.780931image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
terminado 125044
62.7%
cerrado 65834
33.0%
cedido 7404
 
3.7%
suspendido 1269
 
0.6%

Most occurring characters

ValueCountFrequency (%)
r 256712
15.6%
d 208224
12.7%
e 199551
12.1%
o 199551
12.1%
a 190878
11.6%
i 133717
8.1%
n 126313
7.7%
t 125044
7.6%
m 125044
7.6%
C 65834
 
4.0%
Other values (5) 12480
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1576245
95.9%
Uppercase Letter 67103
 
4.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 256712
16.3%
d 208224
13.2%
e 199551
12.7%
o 199551
12.7%
a 190878
12.1%
i 133717
8.5%
n 126313
8.0%
t 125044
7.9%
m 125044
7.9%
c 7404
 
0.5%
Other values (3) 3807
 
0.2%
Uppercase Letter
ValueCountFrequency (%)
C 65834
98.1%
S 1269
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 1643348
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 256712
15.6%
d 208224
12.7%
e 199551
12.1%
o 199551
12.1%
a 190878
11.6%
i 133717
8.1%
n 126313
7.7%
t 125044
7.6%
m 125044
7.6%
C 65834
 
4.0%
Other values (5) 12480
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1643348
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 256712
15.6%
d 208224
12.7%
e 199551
12.1%
o 199551
12.1%
a 190878
11.6%
i 133717
8.1%
n 126313
7.7%
t 125044
7.6%
m 125044
7.6%
C 65834
 
4.0%
Other values (5) 12480
 
0.8%

Modalidad de Contratacion
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.0 MiB
Contratación directa
153859 
Contratación régimen especial
19586 
Mínima cuantía
15972 
Selección Abreviada de Menor Cuantía
 
2506
Selección abreviada subasta inversa
 
2454
Other values (10)
 
5174

Length

Max length59
Median length20
Mean length21.130152
Min length11

Characters and Unicode

Total characters4216543
Distinct characters42
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowContratación directa
2nd rowContratación régimen especial
3rd rowContratación directa
4th rowContratación directa
5th rowContratación directa

Common Values

ValueCountFrequency (%)
Contratación directa 153859
77.1%
Contratación régimen especial 19586
 
9.8%
Mínima cuantía 15972
 
8.0%
Selección Abreviada de Menor Cuantía 2506
 
1.3%
Selección abreviada subasta inversa 2454
 
1.2%
Contratación Directa (con ofertas) 2231
 
1.1%
Contratación régimen especial (con ofertas) 1263
 
0.6%
Licitación pública 545
 
0.3%
No Definido 312
 
0.2%
Licitación pública Obra Publica 306
 
0.2%
Other values (5) 517
 
0.3%

Length

2023-07-19T19:25:35.012649image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
contratación 176939
40.2%
directa 156090
35.4%
régimen 20849
 
4.7%
especial 20849
 
4.7%
cuantía 18478
 
4.2%
mínima 15972
 
3.6%
abreviada 5010
 
1.1%
selección 4960
 
1.1%
con 3504
 
0.8%
ofertas 3494
 
0.8%
Other values (23) 14545
 
3.3%

Most occurring characters

ValueCountFrequency (%)
a 610241
14.5%
t 536474
12.7%
n 424774
10.1%
i 408804
9.7%
c 386758
9.2%
r 369306
8.8%
e 246109
 
5.8%
241139
 
5.7%
o 189224
 
4.5%
ó 182760
 
4.3%
Other values (32) 620954
14.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3754958
89.1%
Space Separator 241139
 
5.7%
Uppercase Letter 211828
 
5.0%
Open Punctuation 3494
 
0.1%
Close Punctuation 3494
 
0.1%
Connector Punctuation 815
 
< 0.1%
Decimal Number 489
 
< 0.1%
Dash Punctuation 326
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 610241
16.3%
t 536474
14.3%
n 424774
11.3%
i 408804
10.9%
c 386758
10.3%
r 369306
9.8%
e 246109
6.6%
o 189224
 
5.0%
ó 182760
 
4.9%
d 161999
 
4.3%
Other values (13) 238509
 
6.4%
Uppercase Letter
ValueCountFrequency (%)
C 180441
85.2%
M 18741
 
8.8%
S 5386
 
2.5%
A 2556
 
1.2%
D 2543
 
1.2%
L 1014
 
0.5%
N 312
 
0.1%
P 306
 
0.1%
O 306
 
0.1%
E 173
 
0.1%
Decimal Number
ValueCountFrequency (%)
2 163
33.3%
0 163
33.3%
1 163
33.3%
Space Separator
ValueCountFrequency (%)
241139
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3494
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3494
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 815
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 326
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3966786
94.1%
Common 249757
 
5.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 610241
15.4%
t 536474
13.5%
n 424774
10.7%
i 408804
10.3%
c 386758
9.7%
r 369306
9.3%
e 246109
6.2%
o 189224
 
4.8%
ó 182760
 
4.6%
C 180441
 
4.5%
Other values (24) 431895
10.9%
Common
ValueCountFrequency (%)
241139
96.5%
( 3494
 
1.4%
) 3494
 
1.4%
_ 815
 
0.3%
- 326
 
0.1%
2 163
 
0.1%
0 163
 
0.1%
1 163
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3977339
94.3%
None 239204
 
5.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 610241
15.3%
t 536474
13.5%
n 424774
10.7%
i 408804
10.3%
c 386758
9.7%
r 369306
9.3%
e 246109
6.2%
241139
 
6.1%
o 189224
 
4.8%
C 180441
 
4.5%
Other values (28) 384069
9.7%
None
ValueCountFrequency (%)
ó 182760
76.4%
í 34450
 
14.4%
é 21143
 
8.8%
ú 851
 
0.4%
Distinct1557
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size3.0 MiB
Minimum2017-12-28 00:00:00
Maximum2023-07-10 00:00:00
2023-07-19T19:25:35.307247image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T19:25:35.575164image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct1546
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size3.0 MiB
Minimum2019-01-01 00:00:00
Maximum2023-07-11 00:00:00
2023-07-19T19:25:35.867180image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T19:25:36.102126image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct1963
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size3.0 MiB
Minimum2019-01-02 00:00:00
Maximum2042-02-23 00:00:00
2023-07-19T19:25:36.680715image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T19:25:36.927227image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct561
Distinct (%)2.2%
Missing174156
Missing (%)87.3%
Memory size3.0 MiB
Minimum2009-01-18 00:00:00
Maximum2021-08-19 00:00:00
2023-07-19T19:25:37.172829image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T19:25:37.414829image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct924
Distinct (%)3.6%
Missing173961
Missing (%)87.2%
Memory size3.0 MiB
Minimum2018-12-07 00:00:00
Maximum2034-11-21 00:00:00
2023-07-19T19:25:37.652546image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T19:25:37.890074image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Valor del Contrato
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct83663
Distinct (%)41.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1282924 × 108
Minimum0
Maximum1.7997072 × 1012
Zeros905
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size3.0 MiB
2023-07-19T19:25:38.119840image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3200000
Q19522000
median18823529
Q337863000
95-th percentile2.1226495 × 108
Maximum1.7997072 × 1012
Range1.7997072 × 1012
Interquartile range (IQR)28341000

Descriptive statistics

Standard deviation4.1973346 × 109
Coefficient of variation (CV)37.20077
Kurtosis169450.81
Mean1.1282924 × 108
Median Absolute Deviation (MAD)11518703
Skewness397.19712
Sum2.2515188 × 1013
Variance1.7617618 × 1019
MonotonicityNot monotonic
2023-07-19T19:25:38.348626image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12000000 1644
 
0.8%
9000000 1489
 
0.7%
6000000 1424
 
0.7%
10000000 1301
 
0.7%
15000000 1299
 
0.7%
18000000 1273
 
0.6%
21000000 1004
 
0.5%
8000000 905
 
0.5%
0 905
 
0.5%
20000000 894
 
0.4%
Other values (83653) 187413
93.9%
ValueCountFrequency (%)
0 905
0.5%
1 3
 
< 0.1%
4 2
 
< 0.1%
10 1
 
< 0.1%
15 1
 
< 0.1%
34 1
 
< 0.1%
81 1
 
< 0.1%
7790 1
 
< 0.1%
17500 1
 
< 0.1%
23500 1
 
< 0.1%
ValueCountFrequency (%)
1.79970725 × 10121
< 0.1%
2.754010013 × 10111
< 0.1%
1.432822371 × 10111
< 0.1%
1.179277155 × 10111
< 0.1%
1.159367591 × 10111
< 0.1%
9.468352547 × 10101
< 0.1%
9.301749043 × 10101
< 0.1%
8.022376125 × 10101
< 0.1%
7.2486 × 10101
< 0.1%
7.127073569 × 10101
< 0.1%

Valor Facturado
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct56685
Distinct (%)28.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45270222
Minimum0
Maximum1.7906829 × 1012
Zeros82836
Zeros (%)41.5%
Negative0
Negative (%)0.0%
Memory size3.0 MiB
2023-07-19T19:25:38.610138image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5800000
Q320620480
95-th percentile67044579
Maximum1.7906829 × 1012
Range1.7906829 × 1012
Interquartile range (IQR)20620480

Descriptive statistics

Standard deviation4.0299543 × 109
Coefficient of variation (CV)89.019981
Kurtosis195337.61
Mean45270222
Median Absolute Deviation (MAD)5800000
Skewness439.67183
Sum9.0337182 × 1012
Variance1.6240532 × 1019
MonotonicityNot monotonic
2023-07-19T19:25:38.836623image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 82836
41.5%
12000000 992
 
0.5%
6000000 894
 
0.4%
9000000 852
 
0.4%
10000000 768
 
0.4%
15000000 763
 
0.4%
18000000 746
 
0.4%
7500000 547
 
0.3%
8000000 546
 
0.3%
21000000 538
 
0.3%
Other values (56675) 110069
55.2%
ValueCountFrequency (%)
0 82836
41.5%
3 1
 
< 0.1%
6 1
 
< 0.1%
20 1
 
< 0.1%
46 1
 
< 0.1%
86 1
 
< 0.1%
669 1
 
< 0.1%
4474 1
 
< 0.1%
7050 1
 
< 0.1%
7790 1
 
< 0.1%
ValueCountFrequency (%)
1.790682932 × 10121
< 0.1%
4.256508058 × 10101
< 0.1%
4.1 × 10101
< 0.1%
3.851396796 × 10101
< 0.1%
3.777576256 × 10101
< 0.1%
3.6525104 × 10101
< 0.1%
3.220546131 × 10101
< 0.1%
3.108968021 × 10101
< 0.1%
3.050190129 × 10101
< 0.1%
2.641529826 × 10101
< 0.1%

Valor Pendiente de Pago
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct62830
Distinct (%)31.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean74860390
Minimum-1.3043882 × 108
Maximum6.0072888 × 1011
Zeros73431
Zeros (%)36.8%
Negative363
Negative (%)0.2%
Memory size3.0 MiB
2023-07-19T19:25:39.102627image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-1.3043882 × 108
5-th percentile0
Q10
median4233018
Q319200000
95-th percentile1.181664 × 108
Maximum6.0072888 × 1011
Range6.0085932 × 1011
Interquartile range (IQR)19200000

Descriptive statistics

Standard deviation1.7676659 × 109
Coefficient of variation (CV)23.612833
Kurtosis70570.8
Mean74860390
Median Absolute Deviation (MAD)4233018
Skewness228.54718
Sum1.4938466 × 1013
Variance3.1246427 × 1018
MonotonicityNot monotonic
2023-07-19T19:25:39.324269image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 73431
36.8%
9000000 752
 
0.4%
12000000 736
 
0.4%
6000000 734
 
0.4%
1 699
 
0.4%
10000000 596
 
0.3%
18000000 577
 
0.3%
15000000 549
 
0.3%
3000000 545
 
0.3%
21000000 469
 
0.2%
Other values (62820) 120463
60.4%
ValueCountFrequency (%)
-130438820 1
< 0.1%
-63433305 1
< 0.1%
-49524712 1
< 0.1%
-41925317 1
< 0.1%
-37314433 1
< 0.1%
-36905725 1
< 0.1%
-36370295 1
< 0.1%
-33634138 1
< 0.1%
-32912750 1
< 0.1%
-31511887 1
< 0.1%
ValueCountFrequency (%)
6.007288838 × 10111
< 0.1%
2.754010013 × 10111
< 0.1%
1.459392215 × 10111
< 0.1%
1.432822371 × 10111
< 0.1%
1.179277155 × 10111
< 0.1%
1.159367591 × 10111
< 0.1%
9.468352547 × 10101
< 0.1%
9.301749043 × 10101
< 0.1%
8.022376125 × 10101
< 0.1%
7.127073569 × 10101
< 0.1%

Valor Pagado
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct52334
Distinct (%)26.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41143799
Minimum0
Maximum1.653768 × 1012
Zeros93285
Zeros (%)46.7%
Negative0
Negative (%)0.0%
Memory size3.0 MiB
2023-07-19T19:25:39.552524image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3247501
Q318424998
95-th percentile61546100
Maximum1.653768 × 1012
Range1.653768 × 1012
Interquartile range (IQR)18424998

Descriptive statistics

Standard deviation3.7228078 × 109
Coefficient of variation (CV)90.482841
Kurtosis195131.95
Mean41143799
Median Absolute Deviation (MAD)3247501
Skewness439.33122
Sum8.2102862 × 1012
Variance1.3859298 × 1019
MonotonicityNot monotonic
2023-07-19T19:25:39.771533image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 93285
46.7%
12000000 874
 
0.4%
6000000 784
 
0.4%
9000000 752
 
0.4%
10000000 681
 
0.3%
15000000 659
 
0.3%
18000000 630
 
0.3%
8000000 474
 
0.2%
21000000 463
 
0.2%
7500000 457
 
0.2%
Other values (52324) 100492
50.4%
ValueCountFrequency (%)
0 93285
46.7%
3 1
 
< 0.1%
6 1
 
< 0.1%
20 1
 
< 0.1%
46 1
 
< 0.1%
86 1
 
< 0.1%
669 1
 
< 0.1%
4474 1
 
< 0.1%
7050 1
 
< 0.1%
7790 1
 
< 0.1%
ValueCountFrequency (%)
1.653768028 × 10121
< 0.1%
4.256508058 × 10101
< 0.1%
4.1 × 10101
< 0.1%
3.851396796 × 10101
< 0.1%
3.6525104 × 10101
< 0.1%
3.255813844 × 10101
< 0.1%
3.220546131 × 10101
< 0.1%
2.629074506 × 10101
< 0.1%
2.622931772 × 10101
< 0.1%
2.600365765 × 10101
< 0.1%

Valor Pendiente de Ejecucion
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct62874
Distinct (%)31.6%
Missing346
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean70963310
Minimum-1.3043882 × 108
Maximum2.75401 × 1011
Zeros73005
Zeros (%)36.6%
Negative357
Negative (%)0.2%
Memory size3.0 MiB
2023-07-19T19:25:40.033531image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-1.3043882 × 108
5-th percentile0
Q10
median4290000
Q319200000
95-th percentile1.1849 × 108
Maximum2.75401 × 1011
Range2.7553144 × 1011
Interquartile range (IQR)19200000

Descriptive statistics

Standard deviation1.0934342 × 109
Coefficient of variation (CV)15.408444
Kurtosis24105.374
Mean70963310
Median Absolute Deviation (MAD)4290000
Skewness122.49927
Sum1.4136246 × 1013
Variance1.1955983 × 1018
MonotonicityNot monotonic
2023-07-19T19:25:40.319049image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 73005
36.6%
9000000 752
 
0.4%
12000000 736
 
0.4%
6000000 735
 
0.4%
1 700
 
0.4%
10000000 597
 
0.3%
18000000 577
 
0.3%
15000000 549
 
0.3%
3000000 543
 
0.3%
21000000 469
 
0.2%
Other values (62864) 120542
60.4%
ValueCountFrequency (%)
-130438820 1
< 0.1%
-63433305 1
< 0.1%
-49524712 1
< 0.1%
-41925317 1
< 0.1%
-37314433 1
< 0.1%
-36905725 1
< 0.1%
-36370295 1
< 0.1%
-33634138 1
< 0.1%
-32912750 1
< 0.1%
-31511887 1
< 0.1%
ValueCountFrequency (%)
2.754010013 × 10111
< 0.1%
1.432822371 × 10111
< 0.1%
1.179277155 × 10111
< 0.1%
1.159367591 × 10111
< 0.1%
9.468352547 × 10101
< 0.1%
9.301749043 × 10101
< 0.1%
8.022376125 × 10101
< 0.1%
7.127073569 × 10101
< 0.1%
6.9486 × 10101
< 0.1%
5.678123846 × 10101
< 0.1%

Saldo CDP
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct70861
Distinct (%)35.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7639314 × 109
Minimum0
Maximum4.3015709 × 1013
Zeros10520
Zeros (%)5.3%
Negative0
Negative (%)0.0%
Memory size3.0 MiB
2023-07-19T19:25:40.584349image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q111266668
median30100000
Q32.214 × 108
95-th percentile4.8087477 × 109
Maximum4.3015709 × 1013
Range4.3015709 × 1013
Interquartile range (IQR)2.1013333 × 108

Descriptive statistics

Standard deviation1.7690117 × 1011
Coefficient of variation (CV)64.003461
Kurtosis52688.64
Mean2.7639314 × 109
Median Absolute Deviation (MAD)24900000
Skewness220.06392
Sum5.5154527 × 1014
Variance3.1294025 × 1022
MonotonicityNot monotonic
2023-07-19T19:25:40.859286image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 10520
 
5.3%
12000000 1240
 
0.6%
15000000 1190
 
0.6%
10000000 1129
 
0.6%
9000000 1005
 
0.5%
6000000 988
 
0.5%
20000000 926
 
0.5%
18000000 910
 
0.5%
30000000 775
 
0.4%
21000000 750
 
0.4%
Other values (70851) 180118
90.3%
ValueCountFrequency (%)
0 10520
5.3%
1 3
 
< 0.1%
15 1
 
< 0.1%
17 1
 
< 0.1%
744 1
 
< 0.1%
876 1
 
< 0.1%
2600 1
 
< 0.1%
21700 1
 
< 0.1%
49000 1
 
< 0.1%
93821 1
 
< 0.1%
ValueCountFrequency (%)
4.301570889 × 10131
 
< 0.1%
4.301160446 × 10132
 
< 0.1%
9.673198774 × 10121
 
< 0.1%
6.750000001 × 10125
< 0.1%
6.11985 × 10121
 
< 0.1%
6.11645 × 10121
 
< 0.1%
6.11325 × 10122
 
< 0.1%
6.10655 × 10123
< 0.1%
6.1065 × 10121
 
< 0.1%
4.138797195 × 10121
 
< 0.1%

Destino Gasto
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.0 MiB
Inversión
129297 
Funcionamiento
69404 
No Definido
 
850

Length

Max length14
Median length9
Mean length10.747523
Min length9

Characters and Unicode

Total characters2144679
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFuncionamiento
2nd rowFuncionamiento
3rd rowFuncionamiento
4th rowInversión
5th rowInversión

Common Values

ValueCountFrequency (%)
Inversión 129297
64.8%
Funcionamiento 69404
34.8%
No Definido 850
 
0.4%

Length

2023-07-19T19:25:41.087282image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-19T19:25:41.303280image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
inversión 129297
64.5%
funcionamiento 69404
34.6%
no 850
 
0.4%
definido 850
 
0.4%

Most occurring characters

ValueCountFrequency (%)
n 467656
21.8%
i 269805
12.6%
e 199551
9.3%
o 140508
 
6.6%
I 129297
 
6.0%
v 129297
 
6.0%
r 129297
 
6.0%
s 129297
 
6.0%
ó 129297
 
6.0%
a 69404
 
3.2%
Other values (10) 351270
16.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1943428
90.6%
Uppercase Letter 200401
 
9.3%
Space Separator 850
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 467656
24.1%
i 269805
13.9%
e 199551
10.3%
o 140508
 
7.2%
v 129297
 
6.7%
r 129297
 
6.7%
s 129297
 
6.7%
ó 129297
 
6.7%
a 69404
 
3.6%
t 69404
 
3.6%
Other values (5) 209912
10.8%
Uppercase Letter
ValueCountFrequency (%)
I 129297
64.5%
F 69404
34.6%
N 850
 
0.4%
D 850
 
0.4%
Space Separator
ValueCountFrequency (%)
850
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2143829
> 99.9%
Common 850
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 467656
21.8%
i 269805
12.6%
e 199551
9.3%
o 140508
 
6.6%
I 129297
 
6.0%
v 129297
 
6.0%
r 129297
 
6.0%
s 129297
 
6.0%
ó 129297
 
6.0%
a 69404
 
3.2%
Other values (9) 350420
16.3%
Common
ValueCountFrequency (%)
850
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2015382
94.0%
None 129297
 
6.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 467656
23.2%
i 269805
13.4%
e 199551
9.9%
o 140508
 
7.0%
I 129297
 
6.4%
v 129297
 
6.4%
r 129297
 
6.4%
s 129297
 
6.4%
a 69404
 
3.4%
t 69404
 
3.4%
Other values (9) 281866
14.0%
None
ValueCountFrequency (%)
ó 129297
100.0%

Dias Adicionados
Real number (ℝ)

SKEWED  ZEROS 

Distinct149
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.41119313
Minimum0
Maximum1160
Zeros198518
Zeros (%)99.5%
Negative0
Negative (%)0.0%
Memory size3.0 MiB
2023-07-19T19:25:41.517445image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1160
Range1160
Interquartile range (IQR)0

Descriptive statistics

Standard deviation8.5639885
Coefficient of variation (CV)20.827168
Kurtosis2345.1162
Mean0.41119313
Median Absolute Deviation (MAD)0
Skewness35.403316
Sum82054
Variance73.341899
MonotonicityNot monotonic
2023-07-19T19:25:41.738998image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 198518
99.5%
30 101
 
0.1%
31 73
 
< 0.1%
214 69
 
< 0.1%
1 40
 
< 0.1%
92 37
 
< 0.1%
4 35
 
< 0.1%
61 31
 
< 0.1%
91 28
 
< 0.1%
244 27
 
< 0.1%
Other values (139) 592
 
0.3%
ValueCountFrequency (%)
0 198518
99.5%
1 40
 
< 0.1%
2 22
 
< 0.1%
3 17
 
< 0.1%
4 35
 
< 0.1%
5 10
 
< 0.1%
6 13
 
< 0.1%
7 19
 
< 0.1%
8 12
 
< 0.1%
9 3
 
< 0.1%
ValueCountFrequency (%)
1160 1
< 0.1%
398 1
< 0.1%
379 1
< 0.1%
369 1
< 0.1%
368 1
< 0.1%
365 1
< 0.1%
350 1
< 0.1%
343 1
< 0.1%
337 2
< 0.1%
335 1
< 0.1%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.0 MiB
No Definido
126227 
Mujer
38338 
Hombre
34801 
Otro
 
185

Length

Max length11
Median length11
Mean length8.9688
Min length4

Characters and Unicode

Total characters1789733
Distinct characters18
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMujer
2nd rowHombre
3rd rowHombre
4th rowNo Definido
5th rowHombre

Common Values

ValueCountFrequency (%)
No Definido 126227
63.3%
Mujer 38338
 
19.2%
Hombre 34801
 
17.4%
Otro 185
 
0.1%

Length

2023-07-19T19:25:41.964673image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-19T19:25:42.189662image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 126227
38.7%
definido 126227
38.7%
mujer 38338
 
11.8%
hombre 34801
 
10.7%
otro 185
 
0.1%

Most occurring characters

ValueCountFrequency (%)
o 287440
16.1%
i 252454
14.1%
e 199366
11.1%
N 126227
7.1%
126227
7.1%
D 126227
7.1%
f 126227
7.1%
n 126227
7.1%
d 126227
7.1%
r 73324
 
4.1%
Other values (8) 219787
12.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1337728
74.7%
Uppercase Letter 325778
 
18.2%
Space Separator 126227
 
7.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 287440
21.5%
i 252454
18.9%
e 199366
14.9%
f 126227
9.4%
n 126227
9.4%
d 126227
9.4%
r 73324
 
5.5%
j 38338
 
2.9%
u 38338
 
2.9%
m 34801
 
2.6%
Other values (2) 34986
 
2.6%
Uppercase Letter
ValueCountFrequency (%)
N 126227
38.7%
D 126227
38.7%
M 38338
 
11.8%
H 34801
 
10.7%
O 185
 
0.1%
Space Separator
ValueCountFrequency (%)
126227
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1663506
92.9%
Common 126227
 
7.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 287440
17.3%
i 252454
15.2%
e 199366
12.0%
N 126227
7.6%
D 126227
7.6%
f 126227
7.6%
n 126227
7.6%
d 126227
7.6%
r 73324
 
4.4%
j 38338
 
2.3%
Other values (7) 181449
10.9%
Common
ValueCountFrequency (%)
126227
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1789733
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 287440
16.1%
i 252454
14.1%
e 199366
11.1%
N 126227
7.1%
126227
7.1%
D 126227
7.1%
f 126227
7.1%
n 126227
7.1%
d 126227
7.1%
r 73324
 
4.1%
Other values (8) 219787
12.3%

Presupuesto General de la Nacion – PGN
Real number (ℝ)

SKEWED  ZEROS 

Distinct25823
Distinct (%)12.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40828738
Minimum0
Maximum1.7997072 × 1012
Zeros161214
Zeros (%)80.8%
Negative0
Negative (%)0.0%
Memory size3.0 MiB
2023-07-19T19:25:42.408658image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile52000000
Maximum1.7997072 × 1012
Range1.7997072 × 1012
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4.0557039 × 109
Coefficient of variation (CV)99.33454
Kurtosis194294.54
Mean40828738
Median Absolute Deviation (MAD)0
Skewness437.96132
Sum8.1474154 × 1012
Variance1.6448734 × 1019
MonotonicityNot monotonic
2023-07-19T19:25:42.629670image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 161214
80.8%
41973750 103
 
0.1%
15000000 101
 
0.1%
20000000 99
 
< 0.1%
30000000 98
 
< 0.1%
10000000 89
 
< 0.1%
24000000 83
 
< 0.1%
12000000 83
 
< 0.1%
27500000 78
 
< 0.1%
18000000 76
 
< 0.1%
Other values (25813) 37527
 
18.8%
ValueCountFrequency (%)
0 161214
80.8%
7790 1
 
< 0.1%
34605 1
 
< 0.1%
43600 1
 
< 0.1%
67400 1
 
< 0.1%
79900 1
 
< 0.1%
85000 1
 
< 0.1%
99000 1
 
< 0.1%
100000 1
 
< 0.1%
109000 1
 
< 0.1%
ValueCountFrequency (%)
1.79970725 × 10121
< 0.1%
7.127073569 × 10101
< 0.1%
5.667932482 × 10101
< 0.1%
5.44 × 10101
< 0.1%
5.242879448 × 10101
< 0.1%
4.2225 × 10101
< 0.1%
4.1 × 10101
< 0.1%
3.8 × 10101
< 0.1%
3.6525104 × 10101
< 0.1%
3.325 × 10101
< 0.1%
Distinct27000
Distinct (%)13.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23031137
Minimum0
Maximum1.1792772 × 1011
Zeros110760
Zeros (%)55.5%
Negative0
Negative (%)0.0%
Memory size3.0 MiB
2023-07-19T19:25:42.892435image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q312150000
95-th percentile41985500
Maximum1.1792772 × 1011
Range1.1792772 × 1011
Interquartile range (IQR)12150000

Descriptive statistics

Standard deviation4.6727841 × 108
Coefficient of variation (CV)20.288985
Kurtosis39870.885
Mean23031137
Median Absolute Deviation (MAD)0
Skewness169.38952
Sum4.5958865 × 1012
Variance2.1834911 × 1017
MonotonicityNot monotonic
2023-07-19T19:25:43.142440image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 110760
55.5%
12000000 1146
 
0.6%
9000000 1076
 
0.5%
6000000 1011
 
0.5%
18000000 875
 
0.4%
10000000 866
 
0.4%
15000000 831
 
0.4%
21000000 730
 
0.4%
8000000 605
 
0.3%
3000000 550
 
0.3%
Other values (26990) 81101
40.6%
ValueCountFrequency (%)
0 110760
55.5%
1 1
 
< 0.1%
39401 2
 
< 0.1%
50000 1
 
< 0.1%
51492 1
 
< 0.1%
73696 1
 
< 0.1%
93580 1
 
< 0.1%
100000 2
 
< 0.1%
110000 1
 
< 0.1%
112000 1
 
< 0.1%
ValueCountFrequency (%)
1.179277155 × 10111
< 0.1%
1.159367591 × 10111
< 0.1%
3.20584563 × 10101
< 0.1%
3.180563051 × 10101
< 0.1%
2.679999908 × 10101
< 0.1%
2.629074506 × 10101
< 0.1%
2.604050006 × 10101
< 0.1%
2.45 × 10101
< 0.1%
2.426897467 × 10101
< 0.1%
2.053847255 × 10101
< 0.1%

Recursos Propios
Real number (ℝ)

SKEWED  ZEROS 

Distinct18687
Distinct (%)9.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13141530
Minimum0
Maximum9.4683525 × 1010
Zeros165174
Zeros (%)82.8%
Negative0
Negative (%)0.0%
Memory size3.0 MiB
2023-07-19T19:25:43.377383image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile32869334
Maximum9.4683525 × 1010
Range9.4683525 × 1010
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.6657558 × 108
Coefficient of variation (CV)27.894437
Kurtosis31307.212
Mean13141530
Median Absolute Deviation (MAD)0
Skewness149.50094
Sum2.6224055 × 1012
Variance1.3437766 × 1017
MonotonicityNot monotonic
2023-07-19T19:25:43.593383image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 165174
82.8%
12000000 242
 
0.1%
6000000 241
 
0.1%
15000000 237
 
0.1%
10000000 214
 
0.1%
9000000 213
 
0.1%
24000000 182
 
0.1%
18000000 171
 
0.1%
20000000 167
 
0.1%
21000000 161
 
0.1%
Other values (18677) 32549
 
16.3%
ValueCountFrequency (%)
0 165174
82.8%
1 1
 
< 0.1%
2 1
 
< 0.1%
61832 1
 
< 0.1%
68000 1
 
< 0.1%
71444 1
 
< 0.1%
93262 1
 
< 0.1%
130900 1
 
< 0.1%
142800 1
 
< 0.1%
150000 2
 
< 0.1%
ValueCountFrequency (%)
9.468352547 × 10101
< 0.1%
6.9486 × 10101
< 0.1%
4.929783084 × 10101
< 0.1%
2.83910293 × 10101
< 0.1%
2.482858222 × 10101
< 0.1%
2.429802354 × 10101
< 0.1%
2.394275368 × 10101
< 0.1%
2.124645046 × 10101
< 0.1%
2.104367381 × 10101
< 0.1%
1.93806224 × 10101
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
False
146971 
True
52580 
ValueCountFrequency (%)
False 146971
73.7%
True 52580
 
26.3%
2023-07-19T19:25:43.806125image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
True
169166 
False
30385 
ValueCountFrequency (%)
True 169166
84.8%
False 30385
 
15.2%
2023-07-19T19:25:44.011108image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

EsGrupo
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
False
198293 
True
 
1258
ValueCountFrequency (%)
False 198293
99.4%
True 1258
 
0.6%
2023-07-19T19:25:44.223342image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

EsPyme
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
False
161970 
True
37581 
ValueCountFrequency (%)
False 161970
81.2%
True 37581
 
18.8%
2023-07-19T19:25:44.414521image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
False
170174 
True
29377 
ValueCountFrequency (%)
False 170174
85.3%
True 29377
 
14.7%
2023-07-19T19:25:44.642063image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
False
191853 
True
 
7698
ValueCountFrequency (%)
False 191853
96.1%
True 7698
 
3.9%
2023-07-19T19:25:44.821013image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
False
198601 
True
 
950
ValueCountFrequency (%)
False 198601
99.5%
True 950
 
0.5%
2023-07-19T19:25:45.014015image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
False
166854 
True
32697 
ValueCountFrequency (%)
False 166854
83.6%
True 32697
 
16.4%
2023-07-19T19:25:45.237144image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Interactions

2023-07-19T19:25:27.366431image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T19:25:04.513034image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T19:25:07.392926image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T19:25:09.823693image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T19:25:12.259612image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T19:25:14.566830image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T19:25:17.180014image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T19:25:19.421336image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T19:25:22.143235image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T19:25:24.568146image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T19:25:27.602461image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T19:25:04.805186image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T19:25:07.642421image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T19:25:10.093369image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T19:25:12.487355image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T19:25:14.863828image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T19:25:17.403626image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T19:25:19.669885image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T19:25:22.397217image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T19:25:24.830494image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T19:25:27.838473image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T19:25:05.109293image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T19:25:07.901719image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T19:25:10.351353image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T19:25:12.720345image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T19:25:15.163857image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T19:25:17.620147image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T19:25:19.901884image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T19:25:22.638235image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T19:25:25.172046image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T19:25:28.054245image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T19:25:05.389265image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T19:25:08.151838image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T19:25:10.582505image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T19:25:12.961893image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T19:25:15.440396image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T19:25:17.832403image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T19:25:20.506884image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T19:25:22.888237image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T19:25:25.573092image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T19:25:28.276228image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T19:25:05.663430image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T19:25:08.408524image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T19:25:10.853618image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T19:25:13.197956image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T19:25:15.691394image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T19:25:18.058319image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T19:25:20.752889image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T19:25:23.131795image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T19:25:25.893610image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T19:25:28.482859image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T19:25:05.994161image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T19:25:08.645677image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T19:25:11.087656image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T19:25:13.430534image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T19:25:15.918394image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T19:25:18.284423image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T19:25:21.017399image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T19:25:23.388084image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T19:25:26.135163image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T19:25:28.675856image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T19:25:06.256206image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T19:25:08.880656image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T19:25:11.328707image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T19:25:13.634518image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T19:25:16.128396image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T19:25:18.500492image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T19:25:21.239413image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T19:25:23.605108image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T19:25:26.371214image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T19:25:28.895860image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T19:25:06.526188image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T19:25:09.110764image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T19:25:11.554726image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T19:25:13.862836image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T19:25:16.358447image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T19:25:18.709495image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T19:25:21.451926image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T19:25:23.853626image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T19:25:26.601431image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T19:25:29.143857image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T19:25:06.828300image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T19:25:09.360745image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T19:25:11.816917image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T19:25:14.103823image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T19:25:16.623484image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T19:25:18.969117image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T19:25:21.687269image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T19:25:24.093638image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T19:25:26.876433image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T19:25:29.372872image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T19:25:07.111367image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T19:25:09.608777image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T19:25:12.050051image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T19:25:14.342815image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T19:25:16.915013image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T19:25:19.200721image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T19:25:21.931236image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T19:25:24.348162image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T19:25:27.133439image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-07-19T19:25:45.813875image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Valor del ContratoValor FacturadoValor Pendiente de PagoValor PagadoValor Pendiente de EjecucionSaldo CDPDias AdicionadosPresupuesto General de la Nacion – PGNRecursos Propios (Alcaldías, Gobernaciones y Resguardos Indígenas)Recursos PropiosDepartamentoOrdenRamaEntidad CentralizadaEstado ContratoModalidad de ContratacionDestino GastoGénero Representante LegalEsServicioPublicoEsPrestacionServiciosEsGrupoEsPymeEstaLiquidadoEsObligacionAmbientalEs PostConflictoEsRecursosPropios
Valor del Contrato1.0000.3300.3830.2820.3840.5390.0050.244-0.0450.0480.0000.0000.0010.0000.0190.0420.0000.0000.0000.0040.0400.0000.0040.0000.0000.000
Valor Facturado0.3301.000-0.5790.918-0.5820.172-0.0260.179-0.039-0.0410.0000.0000.0040.0000.0000.0180.0000.0020.0000.0000.0140.0000.0000.0000.0000.000
Valor Pendiente de Pago0.383-0.5791.000-0.6821.0000.2140.0240.0120.0130.0820.0000.0000.0000.0000.0160.0310.0000.0000.0000.0100.0400.0000.0090.0000.0000.000
Valor Pagado0.2820.918-0.6821.000-0.6850.146-0.0230.170-0.056-0.0400.0000.0000.0040.0000.0000.0180.0000.0020.0000.0000.0140.0000.0000.0000.0000.000
Valor Pendiente de Ejecucion0.384-0.5821.000-0.6851.0000.2150.0240.0120.0130.0820.0000.0020.0000.0050.0210.0360.0000.0000.0000.0230.0410.0000.0150.0000.0000.000
Saldo CDP0.5390.1720.2140.1460.2151.0000.0430.242-0.096-0.0130.0040.0030.0000.0050.0010.0110.0070.0050.0050.0010.0000.0000.0050.0000.0000.001
Dias Adicionados0.005-0.0260.024-0.0230.0240.0431.000-0.027-0.0480.0080.0220.0150.0090.0210.0210.0440.0290.0140.0200.0160.0000.0060.0120.0030.0000.005
Presupuesto General de la Nacion – PGN0.2440.1790.0120.1700.0120.242-0.0271.000-0.403-0.1790.0000.0000.0040.0000.0000.0180.0000.0020.0000.0000.0140.0000.0000.0000.0000.000
Recursos Propios (Alcaldías, Gobernaciones y Resguardos Indígenas)-0.045-0.0390.013-0.0560.013-0.096-0.048-0.4031.000-0.3660.0080.0030.0000.0050.0080.0440.0000.0000.0060.0190.0440.0000.0080.0000.0000.003
Recursos Propios0.048-0.0410.082-0.0400.082-0.0130.008-0.179-0.3661.0000.0000.0140.0060.0080.0210.0420.0000.0000.0040.0180.0520.0000.0120.0000.0000.021
Departamento0.0000.0000.0000.0000.0000.0040.0220.0000.0080.0001.0000.2720.1360.3220.2100.0910.1770.1650.4180.1710.0540.1080.2500.2050.0810.172
Orden0.0000.0000.0000.0000.0020.0030.0150.0000.0030.0140.2721.0000.2430.1210.0710.1590.0670.1580.4150.1720.0210.1400.0990.2220.0740.197
Rama0.0010.0040.0000.0040.0000.0000.0090.0040.0000.0060.1360.2431.0000.0400.0470.1370.1470.0480.1930.1320.0200.0800.1040.0430.0640.313
Entidad Centralizada0.0000.0000.0000.0000.0050.0050.0210.0000.0050.0080.3220.1210.0401.0000.0580.1120.0410.0590.0700.0050.0170.0380.0290.1190.0560.140
Estado Contrato0.0190.0000.0160.0000.0210.0010.0210.0000.0080.0210.2100.0710.0470.0581.0000.1990.0520.0330.0410.1520.1580.0810.0760.0140.0500.042
Modalidad de Contratacion0.0420.0180.0310.0180.0360.0110.0440.0180.0440.0420.0910.1590.1370.1120.1991.0000.2430.1310.2030.6800.4720.5160.3230.0710.0760.189
Destino Gasto0.0000.0000.0000.0000.0000.0070.0290.0000.0000.0000.1770.0670.1470.0410.0520.2431.0000.0470.1200.1460.0050.0810.1050.0870.0120.119
Género Representante Legal0.0000.0020.0000.0020.0000.0050.0140.0020.0000.0000.1650.1580.0480.0590.0330.1310.0471.0000.1230.1730.0520.1770.0980.0830.0120.070
EsServicioPublico0.0000.0000.0000.0000.0000.0050.0200.0000.0060.0040.4180.4150.1930.0700.0410.2030.1200.1231.0000.1050.0000.0670.0390.0990.0230.167
EsPrestacionServicios0.0040.0000.0100.0000.0230.0010.0160.0000.0190.0180.1710.1720.1320.0050.1520.6800.1460.1730.1051.0000.1350.3060.2100.0130.0000.027
EsGrupo0.0400.0140.0400.0140.0410.0000.0000.0140.0440.0520.0540.0210.0200.0170.1580.4720.0050.0520.0000.1351.0000.0100.0790.0020.0100.006
EsPyme0.0000.0000.0000.0000.0000.0000.0060.0000.0000.0000.1080.1400.0800.0380.0810.5160.0810.1770.0670.3060.0101.0000.1680.0420.0000.021
EstaLiquidado0.0040.0000.0090.0000.0150.0050.0120.0000.0080.0120.2500.0990.1040.0290.0760.3230.1050.0980.0390.2100.0790.1681.0000.0370.0000.052
EsObligacionAmbiental0.0000.0000.0000.0000.0000.0000.0030.0000.0000.0000.2050.2220.0430.1190.0140.0710.0870.0830.0990.0130.0020.0420.0371.0000.0100.042
Es PostConflicto0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0810.0740.0640.0560.0500.0760.0120.0120.0230.0000.0100.0000.0000.0101.0000.029
EsRecursosPropios0.0000.0000.0000.0000.0000.0010.0050.0000.0030.0210.1720.1970.3130.1400.0420.1890.1190.0700.1670.0270.0060.0210.0520.0420.0291.000

Missing values

2023-07-19T19:25:29.753270image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-07-19T19:25:31.109746image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-07-19T19:25:32.354042image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

DepartamentoOrdenRamaEntidad CentralizadaEstado ContratoModalidad de ContratacionFecha de FirmaFecha de Inicio del ContratoFecha de Fin del ContratoFecha de Inicio de EjecucionFecha de Fin de EjecucionValor del ContratoValor FacturadoValor Pendiente de PagoValor PagadoValor Pendiente de EjecucionSaldo CDPDestino GastoDias AdicionadosGénero Representante LegalPresupuesto General de la Nacion – PGNRecursos Propios (Alcaldías, Gobernaciones y Resguardos Indígenas)Recursos PropiosEsServicioPublicoEsPrestacionServiciosEsGrupoEsPymeEstaLiquidadoEsObligacionAmbientalEs PostConflictoEsRecursosPropios
2517415AntioquiaNacionalCorporación AutónomaCentralizadaCerradoContratación directa2022-08-012022-08-012022-12-16NaTNaT11892679011892679011892679.037931294Funcionamiento0Mujer0118926790FalseTrueFalseFalseFalseFalseFalseFalse
884993Distrito Capital de BogotáNacionalEjecutivoCentralizadacedidoContratación régimen especial2022-01-052022-01-072022-12-21NaTNaT69310500069310500069310500.069310500Funcionamiento0Hombre0069310500FalseTrueFalseFalseFalseTrueFalseTrue
590266SantanderCorporación AutónomaCorporación AutónomaCentralizadaCerradoContratación directa2022-01-282022-02-012022-09-15NaTNaT12750000127500000127500000.012750000Funcionamiento0Hombre0012750000FalseTrueFalseFalseTrueFalseFalseTrue
1649794BoyacáTerritorialEjecutivoDescentralizadaterminadoContratación directa2022-01-202022-01-212022-09-20NaTNaT24450456244504560244504560.024450456Inversión0No Definido0244504560TrueTrueFalseFalseFalseFalseFalseFalse
1148398AntioquiaTerritorialEjecutivoDescentralizadaterminadoContratación directa2022-08-032022-08-042022-10-19NaTNaT40596004059600040596000.04059600Inversión0Hombre040596000FalseTrueFalseFalseFalseFalseFalseFalse
569984Valle del CaucaTerritorialEjecutivoCentralizadaCerradoContratación directa2020-07-162020-07-172020-10-31NaTNaT19892436019892436019892436.045965395Inversión0No Definido0198924360TrueTrueFalseFalseFalseFalseFalseFalse
1377876BolívarTerritorialEjecutivoDescentralizadaterminadoContratación directa2022-09-282022-09-292022-12-28NaTNaT135000001350000013500000013500000.0100000000Inversión0No Definido000TrueTrueFalseTrueFalseFalseFalseFalse
813532Valle del CaucaTerritorialEjecutivoCentralizadaterminadoContratación directa2020-09-102020-09-162020-12-31NaTNaT12000000012000000012000000.0209300000Inversión0No Definido0120000000TrueTrueFalseFalseFalseFalseFalseFalse
974462HuilaTerritorialCorporación AutónomaDescentralizadaterminadoContratación régimen especial2022-08-022022-08-022022-10-26NaTNaT58500000585000005850000.05850000Funcionamiento0Mujer005850000FalseTrueFalseFalseFalseFalseFalseTrue
221522San Andrés, Providencia y Santa CatalinaTerritorialEjecutivoDescentralizadaCerradoContratación directa2022-02-012022-02-022022-07-02NaTNaT17345800017345800017345800.017345800Inversión0No Definido0173458000FalseTrueFalseFalseFalseFalseFalseFalse
DepartamentoOrdenRamaEntidad CentralizadaEstado ContratoModalidad de ContratacionFecha de FirmaFecha de Inicio del ContratoFecha de Fin del ContratoFecha de Inicio de EjecucionFecha de Fin de EjecucionValor del ContratoValor FacturadoValor Pendiente de PagoValor PagadoValor Pendiente de EjecucionSaldo CDPDestino GastoDias AdicionadosGénero Representante LegalPresupuesto General de la Nacion – PGNRecursos Propios (Alcaldías, Gobernaciones y Resguardos Indígenas)Recursos PropiosEsServicioPublicoEsPrestacionServiciosEsGrupoEsPymeEstaLiquidadoEsObligacionAmbientalEs PostConflictoEsRecursosPropios
2360668SantanderTerritorialEjecutivoCentralizadaterminadoContratación directa2022-02-012022-02-152022-07-15NaTNaT16500000165000000165000000.000000e+000Inversión0Hombre0165000000TrueTrueFalseFalseFalseFalseFalseFalse
1226010Valle del CaucaTerritorialEjecutivoCentralizadaCerradoContratación directa2021-09-292021-10-072021-12-31NaTNaT1103900001103900001.103900e+0711039000Inversión0No Definido0110390000FalseTrueFalseFalseFalseFalseFalseFalse
1747564Valle del CaucaTerritorialCorporación AutónomaCentralizadaCerradoContratación directa2022-01-122022-01-172022-12-30NaTNaT8382000008382000008.382000e+07205198980Inversión0No Definido0838200000FalseTrueFalseFalseFalseFalseFalseFalse
2560718SantanderNacionalEjecutivoDescentralizadaterminadoSelección abreviada subasta inversa2022-04-192022-04-222022-12-10NaTNaT3000000029999481290616019383992.906160e+07394172714Inversión0No Definido3000000000FalseFalseFalseTrueTrueTrueFalseFalse
958926Distrito Capital de BogotáNacionalEjecutivoCentralizadaterminadoSelección abreviada subasta inversa2019-06-062019-05-242019-07-232019-05-242019-07-2310943437160109434371601.094344e+095566918326Funcionamiento0No Definido000FalseFalseFalseTrueTrueFalseFalseFalse
167487Distrito Capital de BogotáTerritorialEjecutivoDescentralizadaterminadoContratación régimen especial2021-01-272021-02-012021-07-31NaTNaT1388012801388012801.388013e+0725654978463Funcionamiento0No Definido0138801280FalseTrueFalseFalseFalseFalseFalseFalse
2291911Distrito Capital de BogotáTerritorialEjecutivoCentralizadacedidoContratación directa2022-01-262022-01-282023-01-27NaTNaT4800000004800000004.800000e+0748000000Inversión0Mujer0480000000FalseTrueFalseFalseFalseFalseFalseFalse
2383512CesarNacionalEjecutivoCentralizadaterminadoContratación régimen especial2020-02-182020-02-182020-12-31NaTNaT13285645190132856451901.328565e+091184822614Inversión0No Definido000FalseFalseFalseFalseFalseFalseFalseFalse
81157BoyacáNacionalEjecutivoDescentralizadaterminadoContratación directa2021-02-172021-02-182021-12-10NaTNaT36351534363515340363515340.000000e+001263021000Inversión0No Definido3635153400FalseTrueFalseFalseFalseFalseFalseFalse
2672506Norte de SantanderTerritorialEjecutivoCentralizadaterminadoContratación directa2020-09-072020-09-082020-12-24NaTNaT10675000106750000106750000.000000e+0010675000Funcionamiento0No Definido0106750000FalseTrueFalseFalseFalseFalseFalseFalse

Duplicate rows

Most frequently occurring

DepartamentoOrdenRamaEntidad CentralizadaEstado ContratoModalidad de ContratacionFecha de FirmaFecha de Inicio del ContratoFecha de Fin del ContratoFecha de Inicio de EjecucionFecha de Fin de EjecucionValor del ContratoValor FacturadoValor Pendiente de PagoValor PagadoValor Pendiente de EjecucionSaldo CDPDestino GastoDias AdicionadosGénero Representante LegalPresupuesto General de la Nacion – PGNRecursos Propios (Alcaldías, Gobernaciones y Resguardos Indígenas)Recursos PropiosEsServicioPublicoEsPrestacionServiciosEsGrupoEsPymeEstaLiquidadoEsObligacionAmbientalEs PostConflictoEsRecursosPropios# duplicates
0Valle del CaucaTerritorialEjecutivoCentralizadaCerradoContratación directa2021-01-252021-01-262021-06-30NaTNaT33450000033450000033450000.033450000Inversión0No Definido0334500000FalseTrueFalseFalseFalseFalseFalseFalse2
1Valle del CaucaTerritorialEjecutivoDescentralizadaCerradoContratación directa2021-07-282021-07-292021-12-31NaTNaT41910000041910000041910000.041910000Inversión0No Definido0419100000FalseTrueFalseFalseFalseFalseFalseFalse2